{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nSUMMARY:\\nDbpedia 14 class/topic classification\\n\\nAccuracy: X\\nTime per Epoch: X seconds = X rev/s\\nTotal time: X*10 = X min = X hours\\nTrain size = 560,000 \\nTest size = 70,000\\n\\nDETAILS:\\nAttempt to replicate crepe model using MXNET:\\nhttps://github.com/zhangxiangxiao/Crepe\\n\\nThis uses an efficient numpy array (dtype=bool)\\nto hold all data in RAM. \\n\\nRun on 1 GPUs (Tesla K80) with batch=128\\nPeak RAM: X\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "SUMMARY:\n",
    "Dbpedia 14 class/topic classification\n",
    "\n",
    "Accuracy: 0.991\n",
    "Time per Epoch: 3403 seconds = 170 rev/s\n",
    "Total time: 33883 seconds = 564 min = 9.5 hours\n",
    "Train size = 560,000 \n",
    "Test size = 70,000\n",
    "\n",
    "DETAILS:\n",
    "Attempt to replicate crepe model using MXNET:\n",
    "https://github.com/zhangxiangxiao/Crepe\n",
    "\n",
    "This uses an efficient numpy array (dtype=bool)\n",
    "to hold all data in RAM. \n",
    "\n",
    "Run on one GPUs (Tesla K80) with batch=128\n",
    "Peak RAM: 60GB\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import mxnet as mx\n",
    "import wget\n",
    "import time\n",
    "import os.path\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AZ_ACC = \"amazonsentimenik\"\n",
    "AZ_CONTAINER = \"textclassificationdatasets\"\n",
    "\n",
    "ALPHABET = list(\"abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\\\"/\\\\|_@#$%^&*~`+ =<>()[]{}\")\n",
    "FEATURE_LEN = 1014\n",
    "BATCH_SIZE = 128\n",
    "NUM_FILTERS = 256\n",
    "DATA_SHAPE = (BATCH_SIZE, 1, FEATURE_LEN, len(ALPHABET))\n",
    "\n",
    "ctx = mx.gpu(2)\n",
    "EPOCHS = 10\n",
    "SD = 0.05  # std for gaussian distribution\n",
    "NOUTPUT = 14  # Classes\n",
    "INITY = mx.init.Normal(sigma=SD)\n",
    "LR = 0.01\n",
    "MOMENTUM = 0.9\n",
    "WDECAY = 0.00001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# logging\n",
    "logger = logging.getLogger()\n",
    "fhandler = logging.FileHandler(filename='crepe_dbp.log', mode='a')\n",
    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
    "fhandler.setFormatter(formatter)\n",
    "logger.addHandler(fhandler)\n",
    "logger.setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def download_file(url):\n",
    "    # Create file-name\n",
    "    local_filename = url.split('/')[-1]\n",
    "    if os.path.isfile(local_filename):\n",
    "        pass\n",
    "        # print(\"The file %s already exist in the current directory\\n\" % local_filename)\n",
    "    else:\n",
    "        # Download\n",
    "        print(\"downloading ...\\n\")\n",
    "        wget.download(url)\n",
    "        print('\\nsaved data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data_frame(infile, shuffle = False):\n",
    "    print(\"processing data frame: %s\" % infile)\n",
    "    # Get data from windows blob\n",
    "    download_file('https://%s.blob.core.windows.net/%s/%s' % (AZ_ACC, AZ_CONTAINER, infile))\n",
    "        # load data into dataframe\n",
    "    df = pd.read_csv(infile,\n",
    "                     header=None,\n",
    "                     names=['sentiment', 'summary', 'text'])\n",
    "    # concat summary, review; trim to 1014 char; reverse; lower\n",
    "    df['rev'] = df.apply(lambda x: \"%s %s\" % (x['summary'], x['text']), axis=1)\n",
    "    df.rev = df.rev.str[:FEATURE_LEN].str[::-1].str.lower()\n",
    "    # store class as nparray\n",
    "    df.sentiment -= 1\n",
    "    y_split = np.asarray(df.sentiment, dtype='int')\n",
    "    # drop columns\n",
    "    df.drop(['text', 'summary', 'sentiment'], axis=1, inplace=True)\n",
    "    if shuffle:\n",
    "        df = df.sample(frac=1).reset_index(drop=True)\n",
    "    # Dictionary to create character vectors\n",
    "    character_hash = pd.DataFrame(np.identity(len(ALPHABET), dtype='bool'), columns=ALPHABET)\n",
    "    print(\"finished processing data frame: %s\" % infile)\n",
    "    print(\"data contains %d obs\" % df.shape[0])\n",
    "    batch_size = df.shape[0]\n",
    "    # Create encoding\n",
    "    X_split = np.zeros([batch_size, 1, FEATURE_LEN, len(ALPHABET)], dtype='bool')\n",
    "    # Main loop\n",
    "    for ti, tx in enumerate(df.rev):\n",
    "        if (ti+1) % (100*1000) == 0:\n",
    "            print(\"Processed: \", ti+1)\n",
    "        chars = list(tx)\n",
    "        for ci, ch in enumerate(chars):\n",
    "            if ch in ALPHABET:\n",
    "                X_split[ti % batch_size][0][ci] = np.array(character_hash[ch], dtype='bool')\n",
    "                \n",
    "    # Return as a DataBatch\n",
    "    #return DataBatch(data=[mx.nd.array(X_split)],\n",
    "    #                 label=[mx.nd.array(y_split[ti + 1 - batch_size:ti + 1])])\n",
    "    return X_split, y_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_crepe():\n",
    "    \"\"\"\n",
    "    Replicating: https://github.com/zhangxiangxiao/Crepe/blob/master/train/config.lua\n",
    "    \"\"\"\n",
    "    input_x = mx.sym.Variable('data')  # placeholder for input\n",
    "    input_y = mx.sym.Variable('softmax_label')  # placeholder for output\n",
    "    # 1. alphabet x 1014\n",
    "    conv1 = mx.symbol.Convolution(\n",
    "        data=input_x, kernel=(7, 69), num_filter=NUM_FILTERS)\n",
    "    relu1 = mx.symbol.Activation(\n",
    "        data=conv1, act_type=\"relu\")\n",
    "    pool1 = mx.symbol.Pooling(\n",
    "        data=relu1, pool_type=\"max\", kernel=(3, 1), stride=(3, 1))\n",
    "    # 2. 336 x 256\n",
    "    conv2 = mx.symbol.Convolution(\n",
    "        data=pool1, kernel=(7, 1), num_filter=NUM_FILTERS)\n",
    "    relu2 = mx.symbol.Activation(\n",
    "        data=conv2, act_type=\"relu\")\n",
    "    pool2 = mx.symbol.Pooling(\n",
    "        data=relu2, pool_type=\"max\", kernel=(3, 1), stride=(3, 1))\n",
    "    # 3. 110 x 256\n",
    "    conv3 = mx.symbol.Convolution(\n",
    "        data=pool2, kernel=(3, 1), num_filter=NUM_FILTERS)\n",
    "    relu3 = mx.symbol.Activation(\n",
    "        data=conv3, act_type=\"relu\")\n",
    "    # 4. 108 x 256\n",
    "    conv4 = mx.symbol.Convolution(\n",
    "        data=relu3, kernel=(3, 1), num_filter=NUM_FILTERS)\n",
    "    relu4 = mx.symbol.Activation(\n",
    "        data=conv4, act_type=\"relu\")\n",
    "    # 5. 106 x 256\n",
    "    conv5 = mx.symbol.Convolution(\n",
    "        data=relu4, kernel=(3, 1), num_filter=NUM_FILTERS)\n",
    "    relu5 = mx.symbol.Activation(\n",
    "        data=conv5, act_type=\"relu\")\n",
    "    # 6. 104 x 256\n",
    "    conv6 = mx.symbol.Convolution(\n",
    "        data=relu5, kernel=(3, 1), num_filter=NUM_FILTERS)\n",
    "    relu6 = mx.symbol.Activation(\n",
    "        data=conv6, act_type=\"relu\")\n",
    "    pool6 = mx.symbol.Pooling(\n",
    "        data=relu6, pool_type=\"max\", kernel=(3, 1), stride=(3, 1))\n",
    "    # 34 x 256\n",
    "    flatten = mx.symbol.Flatten(data=pool6)\n",
    "    # 7.  8704\n",
    "    fc1 = mx.symbol.FullyConnected(\n",
    "        data=flatten, num_hidden=1024)\n",
    "    act_fc1 = mx.symbol.Activation(\n",
    "        data=fc1, act_type=\"relu\")\n",
    "    drop1 = mx.sym.Dropout(act_fc1, p=0.5)\n",
    "    # 8. 1024\n",
    "    fc2 = mx.symbol.FullyConnected(\n",
    "        data=drop1, num_hidden=1024)\n",
    "    act_fc2 = mx.symbol.Activation(\n",
    "        data=fc2, act_type=\"relu\")\n",
    "    drop2 = mx.sym.Dropout(act_fc2, p=0.5)\n",
    "    # 9. 1024\n",
    "    fc3 = mx.symbol.FullyConnected(\n",
    "        data=drop2, num_hidden=NOUTPUT)\n",
    "    crepe = mx.symbol.SoftmaxOutput(\n",
    "        data=fc3, label=input_y, name=\"softmax\")\n",
    "    return crepe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
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       "</g>\r\n",
       "<!-- convolution4&#45;&gt;activation3 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>convolution4&#45;&gt;activation3</title>\r\n",
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       "<g id=\"node12\" class=\"node\"><title>activation4</title>\r\n",
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       "</g>\r\n",
       "<!-- activation4&#45;&gt;convolution4 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>activation4&#45;&gt;convolution4</title>\r\n",
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       "</g>\r\n",
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       "<text text-anchor=\"middle\" x=\"47\" y=\"-1239.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">relu</text>\r\n",
       "</g>\r\n",
       "<!-- activation5&#45;&gt;convolution5 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>activation5&#45;&gt;convolution5</title>\r\n",
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       "</g>\r\n",
       "<!-- pooling2 -->\r\n",
       "<g id=\"node15\" class=\"node\"><title>pooling2</title>\r\n",
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       "<!-- pooling2&#45;&gt;activation5 -->\r\n",
       "<g id=\"edge14\" class=\"edge\"><title>pooling2&#45;&gt;activation5</title>\r\n",
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       "<!-- flatten0 -->\r\n",
       "<g id=\"node16\" class=\"node\"><title>flatten0</title>\r\n",
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       "</g>\r\n",
       "<!-- flatten0&#45;&gt;pooling2 -->\r\n",
       "<g id=\"edge15\" class=\"edge\"><title>flatten0&#45;&gt;pooling2</title>\r\n",
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       "</g>\r\n",
       "<!-- fullyconnected0 -->\r\n",
       "<g id=\"node17\" class=\"node\"><title>fullyconnected0</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"47\" y=\"-1521.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">1024</text>\r\n",
       "</g>\r\n",
       "<!-- fullyconnected0&#45;&gt;flatten0 -->\r\n",
       "<g id=\"edge16\" class=\"edge\"><title>fullyconnected0&#45;&gt;flatten0</title>\r\n",
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       "<!-- activation6 -->\r\n",
       "<g id=\"node18\" class=\"node\"><title>activation6</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"47\" y=\"-1630.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Activation</text>\r\n",
       "<text text-anchor=\"middle\" x=\"47\" y=\"-1615.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">relu</text>\r\n",
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       "<!-- dropout0 -->\r\n",
       "<g id=\"node19\" class=\"node\"><title>dropout0</title>\r\n",
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       "<!-- dropout0&#45;&gt;activation6 -->\r\n",
       "<g id=\"edge18\" class=\"edge\"><title>dropout0&#45;&gt;activation6</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"47\" y=\"-1803.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">1024</text>\r\n",
       "</g>\r\n",
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       "<g id=\"edge19\" class=\"edge\"><title>fullyconnected1&#45;&gt;dropout0</title>\r\n",
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       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x1d135d68>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualise symbol (for crepe)\n",
    "crepe = create_crepe()\n",
    "\n",
    "a = mx.viz.plot_network(crepe)\n",
    "a.render('Crepe Model')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing data frame: dbpedia_train.csv\n",
      "finished processing data frame: dbpedia_train.csv\n",
      "data contains 560000 obs\n",
      "('Processed: ', 100000)\n",
      "('Processed: ', 200000)\n",
      "('Processed: ', 300000)\n",
      "('Processed: ', 400000)\n",
      "('Processed: ', 500000)\n"
     ]
    }
   ],
   "source": [
    "train_x, train_y = load_data_frame('dbpedia_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing data frame: dbpedia_test.csv\n",
      "finished processing data frame: dbpedia_test.csv\n",
      "data contains 70000 obs\n"
     ]
    }
   ],
   "source": [
    "test_x, test_y = load_data_frame('dbpedia_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(560000L, 1L, 1014L, 69L)\n",
      "(560000L,)\n",
      "(70000L, 1L, 1014L, 69L)\n",
      "(70000L,)\n"
     ]
    }
   ],
   "source": [
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_iter = mx.io.NDArrayIter(train_x, train_y, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_iter = mx.io.NDArrayIter(test_x, test_y, batch_size=BATCH_SIZE, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del train_x\n",
    "del train_y\n",
    "del test_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = mx.model.FeedForward(\n",
    "    ctx = ctx,\n",
    "    symbol = create_crepe(), \n",
    "    num_epoch = EPOCHS,  # number of training rounds\n",
    "    learning_rate = LR,  # learning rate\n",
    "    momentum = MOMENTUM,   # momentum for sgd\n",
    "    wd = WDECAY,  # weight decay for reg\n",
    "    initializer = INITY  # init with sd of 0.05\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished training in 33883 seconds\n"
     ]
    }
   ],
   "source": [
    "tic = time.time()\n",
    "model.fit(\n",
    "    X = train_iter,\n",
    "    eval_metric=['accuracy'],\n",
    "    batch_end_callback=mx.callback.Speedometer(BATCH_SIZE),\n",
    "    epoch_end_callback=mx.callback.do_checkpoint(\"crepe_dbp_chck_\") \n",
    ")\n",
    "print(\"Finished training in %.0f seconds\" % (time.time() - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Predict\n",
    "pred = np.argsort(model.predict(X = test_iter))[:,-1]\n",
    "\n",
    "# Save Results\n",
    "np.savetxt('crepe_predict_class_dbpedia.csv', np.c_[pred, test_y], delimiter=',', fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99058571428571429"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy\n",
    "acc = sum(pred==test_y.astype('int'))/float(len(test_y))\n",
    "logger.info(acc)\n",
    "acc "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log\n",
    "\n",
    "\n",
    "```\n",
    "2016-08-30 00:37:37,993 - root - INFO - Start training with [gpu(2)]\n",
    "2016-08-30 00:38:48,349 - root - INFO - Epoch[0] Batch [50]\tSpeed: 123.69 samples/sec\tTrain-accuracy=0.911563\n",
    "2016-08-30 00:39:32,703 - root - INFO - Epoch[0] Batch [100]\tSpeed: 144.30 samples/sec\tTrain-accuracy=0.926875\n",
    "2016-08-30 00:40:14,904 - root - INFO - Epoch[0] Batch [150]\tSpeed: 151.65 samples/sec\tTrain-accuracy=0.931875\n",
    "2016-08-30 00:40:55,510 - root - INFO - Epoch[0] Batch [200]\tSpeed: 157.61 samples/sec\tTrain-accuracy=0.926406\n",
    "2016-08-30 00:41:36,693 - root - INFO - Epoch[0] Batch [250]\tSpeed: 155.41 samples/sec\tTrain-accuracy=0.928281\n",
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    "2016-08-30 09:54:07,444 - root - INFO - Epoch[9] Batch [3750]\tSpeed: 170.65 samples/sec\tTrain-accuracy=0.997344\n",
    "2016-08-30 09:54:46,607 - root - INFO - Epoch[9] Batch [3800]\tSpeed: 163.48 samples/sec\tTrain-accuracy=0.997969\n",
    "2016-08-30 09:55:24,372 - root - INFO - Epoch[9] Batch [3850]\tSpeed: 169.46 samples/sec\tTrain-accuracy=0.998750\n",
    "2016-08-30 09:56:03,174 - root - INFO - Epoch[9] Batch [3900]\tSpeed: 164.94 samples/sec\tTrain-accuracy=0.998125\n",
    "2016-08-30 09:56:42,539 - root - INFO - Epoch[9] Batch [3950]\tSpeed: 162.64 samples/sec\tTrain-accuracy=0.996719\n",
    "2016-08-30 09:57:24,753 - root - INFO - Epoch[9] Batch [4000]\tSpeed: 151.61 samples/sec\tTrain-accuracy=0.996094\n",
    "2016-08-30 09:58:02,507 - root - INFO - Epoch[9] Batch [4050]\tSpeed: 169.51 samples/sec\tTrain-accuracy=0.998281\n",
    "2016-08-30 09:58:41,924 - root - INFO - Epoch[9] Batch [4100]\tSpeed: 162.37 samples/sec\tTrain-accuracy=0.999062\n",
    "2016-08-30 09:59:21,043 - root - INFO - Epoch[9] Batch [4150]\tSpeed: 163.67 samples/sec\tTrain-accuracy=0.997969\n",
    "2016-08-30 10:00:00,878 - root - INFO - Epoch[9] Batch [4200]\tSpeed: 160.67 samples/sec\tTrain-accuracy=0.995938\n",
    "2016-08-30 10:00:40,346 - root - INFO - Epoch[9] Batch [4250]\tSpeed: 162.15 samples/sec\tTrain-accuracy=0.994844\n",
    "2016-08-30 10:01:19,168 - root - INFO - Epoch[9] Batch [4300]\tSpeed: 164.85 samples/sec\tTrain-accuracy=0.997500\n",
    "2016-08-30 10:01:58,898 - root - INFO - Epoch[9] Batch [4350]\tSpeed: 161.09 samples/sec\tTrain-accuracy=0.997344\n",
    "2016-08-30 10:02:20,362 - root - INFO - Epoch[9] Resetting Data Iterator\n",
    "2016-08-30 10:02:20,362 - root - INFO - Epoch[9] Time cost=3403.647\n",
    "2016-08-30 10:02:21,410 - root - INFO - Saved checkpoint to \"crepe_dbp_chck_-0010.params\"\n",
    "2016-08-30 10:03:32,533 - root - INFO - 0.990585714286\n",
    "```"
   ]
  }
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